Center for Computational Imaging and Personalized Diagnostics Publications

Wednesday, January 1, 2020

A Deep Learning-Based Approach for Localization and Diagnosis of Non-Melanoma Skin Cancers from Whole Slide Digital Pathology Images

Zhou, Y., Janowczyk, A., Lu, C., Grobholz, R., Katz, I., & Madabhushi, A. (2020). A Deep Learning-Based Approach for Localization and Diagnosis of Non-Melanoma Skin Cancers from Whole Slide Digital Pathology Images. , 100 (SUPPL 1), 518--519.

Wednesday, January 1, 2020

Computationally Derived Morphological Features of Cancer Nuclei from Colon Whole Slide Images Can Distinguish Stage 2 from Stage 4 Colon Cancers

Kumar, N., Lu, C., Butler, K., Gilmore, H., Willis, J., & Madabhushi, A. (2020). Computationally Derived Morphological Features of Cancer Nuclei from Colon Whole Slide Images Can Distinguish Stage 2 from Stage 4 Colon Cancers. , 100 (SUPPL 1), 699--700.

Wednesday, January 1, 2020

T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology

Shiradkar, R., Panda, A., Leo, P., Janowczyk, A., Farre, X., Janaki, N., ... Madabhushi, A. (2020). T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology. European Radiology, ().

Wednesday, January 1, 2020

Computerized Nuclear Morphology Features Aid in Diagnosis of Cell Clusters on Digitized Bile Duct Brushing Slides

Monabbati, S., Lu, C., Leo, P., Bera, K., Madabhushi, A., Harbhajanka, A., ... Nezami, B. (2020). Computerized Nuclear Morphology Features Aid in Diagnosis of Cell Clusters on Digitized Bile Duct Brushing Slides. , 100 (SUPPL 1), 1662--1663.

Wednesday, January 1, 2020

Predicting HPV Status of Oropharyngeal Squamous Cell Carcinoma Patients Using Handcrafted Histomorphometric and Deep Learning Features

Koyuncu, C., Lu, C., Gondim, D., Bera, K., Thompson, L., Lewis, J., ... Madabhushi, A. (2020). Predicting HPV Status of Oropharyngeal Squamous Cell Carcinoma Patients Using Handcrafted Histomorphometric and Deep Learning Features. , 100 (SUPPL 1), 1207--1207.

Wednesday, January 1, 2020

The Spatial Patterns of Tumor-Infiltrating Lymphocytes (TILs) are More Prognostic Than TIL Density in p16+ Oropharyngeal Squamous Cell Carcinoma Patients

Corredor-Prada, G., Lewis, J., Lu, C., Toro, P., Fu, P., Thorstad, W., ... Madabhushi, A. (2020). The Spatial Patterns of Tumor-Infiltrating Lymphocytes (TILs) are More Prognostic Than TIL Density in p16+ Oropharyngeal Squamous Cell Carcinoma Patients. , 100 (SUPPL 1), 1187--1188.

Wednesday, January 1, 2020

Tumor Cell Multinucleation is More Frequent in African-American Oropharyngeal Squamous Cell Carcinoma Patients Than Caucasian-American Ones-Implications for Outcome Differences

Koyuncu, C., Lu, C., Zhang, Z., Fu, P., Gondim, D., Xue, Z., ... Madabhushi, A. (2020). Tumor Cell Multinucleation is More Frequent in African-American Oropharyngeal Squamous Cell Carcinoma Patients Than Caucasian-American Ones-Implications for Outcome Differences. , 100 (SUPPL 1), 1208--1209.

Wednesday, January 1, 2020

A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer

Hiremath, A., Shiradkar, R., Braman, N., Prasanna, P., Rastinehad, A., Purysko, A., ... Madabhushi, A. (2020). A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer. , 11314 ().

Wednesday, January 1, 2020

Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: Preliminary results

Shiradkar, R., Zuo, R., Mahran, A., Ponsky, L., Tirumani, S., & Madabhushi, A. (2020). Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: Preliminary results. , 11314 ().

Wednesday, January 1, 2020

Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in NonSmall Cell Lung Cancer

Khorrami, M., Prasanna, P., Gupta, A., Patil, P., Velu, P., Thawani, R., ... Madabhushi, A. (2020). Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in NonSmall Cell Lung Cancer. Cancer Immunology Research, 8 (1), 108-119.

Wednesday, January 1, 2020

Imaging Features of Vessels and Leakage Patterns Predict Extended Interval Aflibercept Dosing Using Ultra-Widefield Angiography in Retinal Vascular Disease: Findings from the PERMEATE Study

Moosavi, A., Figueiredo, N., Prasanna, P., K. Srivastava, S., Sharma, S., Madabhushi, A., ... Ehlers, J. (2020). Imaging Features of Vessels and Leakage Patterns Predict Extended Interval Aflibercept Dosing Using Ultra-Widefield Angiography in Retinal Vascular Disease: Findings from the PERMEATE Study. IEEE Transactions on Biomedical Engineering, (), 1-1.

Wednesday, January 1, 2020

Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade

Vaidya, P., Bera, K., Patil, P., Gupta, A., Jain, P., Alilou, M., ... Madabhushi, A. (2020). Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade. Journal for immunotherapy of cancer, 8 (2).

Wednesday, January 1, 2020

Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy

Braman, N., Prasanna, P., Bera, K., Alilou, M., Vulchi, M., Etesami, M., ... Madabhushi, A. (2020). Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy. , 80 (4).

Wednesday, January 1, 2020

Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability

Prasanna, P., Bobba, V., Figueiredo, N., Sevgi, D., Lu, C., Braman, N., ... Others (2020). Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability. British Journal of Ophthalmology, ().

Sunday, December 1, 2019

Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study

Viswanath, S., Chirra, P., Yim, M., Rofsky, N., Purysko, A., Rosen, M., ... Madabhushi, A. (2019). Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study. BMC Medical Imaging, 19 (1).

Sunday, December 1, 2019

Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study

Prasanna, P., Mitra, J., Beig, N., Nayate, A., Patel, S., Ghose, S., ... Tiwari, P. (2019). Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study. Scientific Reports, 9 (1).

Friday, November 8, 2019

Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer

Merisaari, H., Taimen, P., Shiradkar, R., Ettala, O., Persola, M., Saunavaara, J., ... Jambor, I. (2019). Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer. Magnetic Resonance in Imaging, 83 (6), 2293 - 2309.

Friday, November 1, 2019

Artificial intelligence in digital pathology � new tools for diagnosis and precision oncology

Bera, K., Schalper, K., Rimm, D., Velcheti, V., & Madabhushi, A. (2019). Artificial intelligence in digital pathology � new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology, 16 (11), 703-715.

Thursday, October 17, 2019

Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings

Li, H., Whitney, J., Bera, K., Gilmore, H., Thorat, M., Badve, S., ... Madabhushi, A. (2019). Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings. Breast Cancer Research, 21 (1), 114.

Tuesday, October 1, 2019

Corrigendum to �Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features� [Lung Cancer 135 (September) (2019) 1�9]

Khorrami, M., Jain, P., Bera, K., Alilou, M., Thawani, R., Patil, P., ... Madabhushi, A. (2019). Corrigendum to �Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features� [Lung Cancer 135 (September) (2019) 1�9]. Lung Cancer, 136 ().

Sunday, September 1, 2019

Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features

Khorrami, M., Jain, P., Bera, K., Alilou, M., Thawani, R., Patil, P., ... Madabhushi, A. (2019). Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features. Lung Cancer, 135 (), 1-9.

Monday, July 1, 2019

Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines

Feeny, A., Rickard, J., Patel, D., Toro, S., Trulock, K., Park, C., ... Chung, M. (2019). Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines. Circulation - Arrhythmia and Electrophysiology, 12 (7).

Friday, June 14, 2019

Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI

Chirra, P., Leo, P., Yim, M., Bloch, B., Rastinehad, A., Purysko, A., ... Viswanath, S. (2019). Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI. Journal of Medical Imaging, 6 (02).

Saturday, June 1, 2019

Applications of machine learning in drug discovery and development

Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., ... Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18 (6), 463-477.

Saturday, June 1, 2019

The revolving door for AI and pathologists�docendo discimus?

Van Es, S., & Madabhushi, A. (2019). The revolving door for AI and pathologists�docendo discimus?. Journal of Medical Artificial Intelligence, 2 (), 12-12.

Case School of Engineering

Center for Computational Imaging and Personalized Diagnostics

Center for Computational Imaging and Personalized Diagnostics Publications

A Deep Learning-Based Approach for Localization and Diagnosis of Non-Melanoma Skin Cancers from Whole Slide Digital Pathology Images

Computationally Derived Morphological Features of Cancer Nuclei from Colon Whole Slide Images Can Distinguish Stage 2 from Stage 4 Colon Cancers

T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning–derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology

Computerized Nuclear Morphology Features Aid in Diagnosis of Cell Clusters on Digitized Bile Duct Brushing Slides

Predicting HPV Status of Oropharyngeal Squamous Cell Carcinoma Patients Using Handcrafted Histomorphometric and Deep Learning Features

The Spatial Patterns of Tumor-Infiltrating Lymphocytes (TILs) are More Prognostic Than TIL Density in p16+ Oropharyngeal Squamous Cell Carcinoma Patients

Tumor Cell Multinucleation is More Frequent in African-American Oropharyngeal Squamous Cell Carcinoma Patients Than Caucasian-American Ones-Implications for Outcome Differences

A combination of intra- and peri-tumoral deep features from prostate bi-parametric MRI can distinguish clinically significant and insignificant prostate cancer

Radiomic features derived from periprostatic fat on pre-surgical T2w MRI predict extraprostatic extension of prostate cancer identified on post-surgical pathology: Preliminary results

Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in NonSmall Cell Lung Cancer

Imaging Features of Vessels and Leakage Patterns Predict Extended Interval Aflibercept Dosing Using Ultra-Widefield Angiography in Retinal Vascular Disease: Findings from the PERMEATE Study

Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade

Radiomic measurements of tumor-associated vasculature morphology and function on pretreatment dynamic MRI identifies responders to neoadjuvant chemotherapy

Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability

Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study

Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study

Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer

Artificial intelligence in digital pathology � new tools for diagnosis and precision oncology

Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings

Corrigendum to �Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features� [Lung Cancer 135 (September) (2019) 1�9]

Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features

Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines

Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI

Applications of machine learning in drug discovery and development

The revolving door for AI and pathologists�docendo discimus?

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